annoy.

The nearest neighbor problem is very common in data science. It's useful in recommender situations but also with neural embeddings in general. It's an expensive thing to calculate so it is common to calculate approximate distances as a proxy. In python a very likeable tool for this is annoy.

Intro Code Plotting How it Works 1 How it Works 2 Accuracy Benchmark Final Features

Prev Video

Next Video

Episode Notes

All the code below will generate the same plot;

import numpy as np
import matplotlib.pylab as plt
from annoy import AnnoyIndex

columns = 2

vecs = np.concatenate([
    np.random.normal(-1, 1, (5000, columns)), 
    np.random.normal(0, 0.5, (5000, columns)),
])

annoy = AnnoyIndex(columns, 'euclidean')
for i in range(vecs.shape[0]):
    annoy.add_item(i, vecs[i, :])
annoy.build(n_trees=1)

plt.figure(figsize=(5, 5))
plt.scatter(vecs[:, 0], vecs[:, 1], s=1);

indices = annoy.get_nns_by_vector(np.array([-1., -1.]), 2000)

subset = vecs[indices, :]
plt.scatter(subset[:, 0], subset[:, 1], s=1);

Feedback? See an issue? Feel free to mention it here.

If you want to be kept up to date, consider getting the newsletter.